Smart Supply Chain Management and Big Data Analysis Using Machine Learning in Industry 4.0

The manufacturing sector has been significantly influenced by the area of machine learning (ML) within the framework of the sector 4.0 concept. The concept of Industry 4.0 promotes the use of intelligent sensors, gadgets, and machinery in order to facilitate the development of intelligent factories that consistently gather production-related data. Machine learning approaches facilitate the production of practical insights by analyzing the gathered data in order to enhance industrial efficiency while minimizing the need for substantial resource modifications. Moreover, the utilization of machine learning (ML) techniques has facilitated the identification of intricate manufacturing patterns and presents an opportunity for the development of an intelligent decision support system in various manufacturing domains. These domains include intelligent and continuous inspection, predictive maintenance, quality enhancement, process optimization, supply chain management, and task scheduling. Various machine learning (ML) techniques have been employed in diverse manufacturing applications previously. However, numerous unresolved inquiries and difficulties persist in this domain. These include the curation, storage, and comprehension of large datasets, the ability to reason with data to facilitate real-time actionable insights, as well as concerns related to edge computing and the cybersecurity aspects of smart manufacturing. Therefore, the primary objective of this special edition is to assemble a diverse group of scholars who will present the most recent advancements in both the theoretical underpinnings and practical implementations of machine learning (ML) within the context of manufacturing and production systems.